Atlas for automatic segmentation of retina layers from OCT images

ABSTRACT

A method for segmentation of a 3-D medical image uses an adaptive patient-specific atlas and an appearance model for 3-D Optical Coherence Tomography (OCT) data. For segmentation of a medical image of a retina, In order to reconstruct the 3-D patient-specific retinal atlas, a 2-D slice of the 3-D image containing the macula mid-area is segmented first. A 2-D shape prior is built using a series of co-aligned training OCT images. The shape prior is then adapted to the first order appearance and second order spatial interaction MGRF model of the image data to be segmented. Once the macula mid-area is segmented into separate retinal layers this initial slice, the segmented layers&#39; labels and their appearances are used to segment the adjacent slices. This step is iterated until the complete 3-D medical image is segmented.

This application claims the benefit of U.S. provisional patentapplication Ser. No. 62/663,011, filed 26 Apr. 2018, for ATLAS FORAUTOMATIC SEGMENTATION OF RETINA LAYERS FROM OCT IMAGES, incorporatedherein by reference.

FIELD OF THE INVENTION

A method for segmentation of a 3-D medical image uses an adaptivepatient-specific atlas and an appearance model for 3-D Optical CoherenceTomography (OCT) data. For segmentation of a medical image of a retina,In order to reconstruct the 3-D patient-specific retinal atlas, a 2-Dslice of the 3-D image containing the macula mid-area is segmentedfirst. A 2-D shape prior is built using a series of co-aligned trainingOCT images. The shape prior is then adapted to the first orderappearance and second order spatial interaction MGRF model of the imagedata to be segmented. Once the macula mid-area is segmented intoseparate retinal layers in this initial slice, the segmented layers'labels and their appearances are used to segment the adjacent slices.This step is iterated until the complete 3-D medical image is segmented.

BACKGROUND

There are approximately 415 million adults living with diabetesworldwide and approximately 642 million could be affected by 2040. Thoseaffected are at risk for degraded vision due to diabetic retinopathy, inwhich damage to the blood vessels leads to fluid accumulation in theretinal layers. It is crucial to monitor and detect changes in themorphology and appearance of retinal layers in order to prevent loss ofvision in diabetic patients. There are several medical imagingmodalities that can be used to observe qualitative and quantitativeanatomical features of the retina, such as fundus imaging and opticalcoherence tomography (OCT). Fundus imaging produces a 2-D representationof the retina where the reflected amount of light is represented byimage intensities. Fundus imaging is limited in that it does not provideany indication of depth, i.e. the particular layers of retina wherechanges occur. OCT overcomes this limitation with the ability tononinvasively image internal tissue of the human body in cross sectionat micron resolutions through measuring the reflections of light waves.OCT is used heavily by ophthalmologists and optometrists for obtaininghigh-resolution images of the retina and anterior segment of the eye.The modality has found widespread application such as vascular plaquedetection, lumen detection, and cancer detection.

A volumetric OCT scan includes of a set of A-scans representingreflectivity of retinal tissue as a function of depth beneath a singlepoint on the surface. The juxtaposition of A-scans taken along a lineacross the surface provides a cross-sectional image or B-scan, a 2-Dmedical image. Volumetric OCT, a 3-D medical image, comprises an arrayof adjacent B-scans. To be able to quantify both the morphology andreflectivity of a certain layer of retina, a user must segment thevolume into layers and extract the layer of interest from the volume.The typical method to extract the layer of interest is to use imageediting software to manually delineate retinal layers. However, thismethod is time consuming, subjective, and dependent on the experienceand skill of the individual performing the manual segmentation.

Several approaches to automatic segmentation have been proposed. Mostcan be classified as: 1) threshold methods, 2) level set basedtechniques, and 3) graph cut techniques. However, the existingtechniques suffer from at least one of several limitations: 1)segmentation of retinal layers is derived from only a single B-scan(e.g., a cross section of the macula), 2) coarse segmentation of theretina into fewer than the approximately 12 layers that can bedistinguished by OCT, and 3) long execution times, particularly forgraph-based approaches, rendering the automatic segmentation techniquesunsuitable for clinical use.

SUMMARY

To address these limitations, disclosed herein is a novel, fast,patient-specific approach to segmentation of OCT retinal image data thatincludes selecting a B-scan extending through the center of the fovea,segmenting the slice into a plurality of retinal layers, then using theobtained segmentation data to drive the segmentation of adjacent B-scansrecursively. This approach is able to segment all 12 layers of retinaand, to the best of the inventors' knowledge, will be the first fast 3-Dsegmentation approach with such capabilities which makes it suitable fora clinical setting.

This new approach uses patient-specific anatomy of retinal layers toaccurately segment the retinal image. The accuracy and speed of theinstant 3-D segmentation approach is highlighted by comparing resultswith those generated by a state-of-the-art 3-D OCT segmentationapproach. Extracting the patient-specific atlas that describes bothanatomy and appearance/reflectivity from the central OCT image andpropagating this atlas to segment the adjacent images results inincreased speed and accuracy as compared to existing techniques. Whilethe proposed approach was developed to segment OCT images, itsunderlying concept can be generalized to segment organs from other 3-Dmedical imaging modalities such as CT and MRI.

It will be appreciated that the various apparatus and methods describedin this summary section, as well as elsewhere in this application, canbe expressed as a large number of different combinations andsubcombinations. All such useful, novel, and inventive combinations andsubcombinations are contemplated herein, it being recognized that theexplicit expression of each of these combinations is unnecessary.

BRIEF DESCRIPTION OF THE DRAWINGS

A better understanding of the present invention will be had uponreference to the following description in conjunction with theaccompanying drawings.

FIG. 1 is a flowchart illustrating a method for OCT 3-D macular volumesegmentation.

FIG. 2 depicts an alignment procedure for an input OCT image (A),highlighting the large scale structure of the retina in (B). Multiscaleedges near the foveal peak, inside the bounded region of (A) are shownin (C). At the finest level of detail, three boundaries are detected(D).

FIG. 3 depicts an illustration of the midline B-scan segmentationpropagation process into the whole B-scan volume.

FIG. 4 depicts an illustration of voxel labeling in patient-specificatlas-based (propagation) segmentation process.

FIG. 5 depicts an illustrative example of the execution steps on a5-scan 3-D OCT volume showing how macula mid-slice segmentation in (Step1) is used to drive the segmentation of adjacent slices (Step 2) and theuse of resulting segmentation from (Step 2) to drive the segmentation offurther adjacent slices (Step 3).

FIG. 6 depicts a comparison of retinal image segmentation generated by(top row) ground truth, (middle row) the disclosed segmentationapproach, and (bottom row) a prior art segmentation approach.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

Referring now to FIG. 1, the depicted novel framework possesses theability to accurately segment 13 distinct surfaces, namely, 12 retinallayers, as well as the vitreous and the choroid, from OCT volumes. Theproposed framework proceeds into two stages: stage 1, an algorithmic 2-Dsegmentation of the B-scan extending through the center of the fovea(also referred to as the midline B-scan, macula mid-slice, or theinitial slice), then stage 2, where the inherited segmentationinformation from the midline B-scan image propagates outward from themidline B-scan image to aid segmentation of the entire 3-D volume asexplained in Section B. In further detail, a method for segmentation ofmedical images includes 10 —providing a 3-D medical image (e.g., a3D-OCT retinal image), 12—selecting an initial 2-D slice of the 3-Dmedical image, preferably at or near the center of the 3-D image (e.g, aslice bisecting the foveal pit), 14—aligning the initial image slice toa constructed shape database (e.g., a database comprising differentfovea images collected from different healthy and diseased subjects),16—applying a joint model to the aligned initial image slice, and18—obtaining a final segmentation of the initial slice (e.g., segmentingthe 12 of layers of the retina in the initial slice). Steps 12 through18 comprise the first stage. Continuing to the second stage, the method10 includes 20—selecting a current image slice adjacent to thepreviously segmented image slice, 22—aligning the current image slice tothe previously segmented image slice image, and 24—segmenting thecurrent image slice based at least in part upon the previously segmentedimage slice. Steps 20 through 24 comprising the second stage are thenrepeated to progressively segment the entire volume of the medicalimage. Once segmented, the plurality of 2-D slices may be reassembledinto a 3-D image for convenient visualization of the segmented image.Steps of the first stage and second stage are described in furtherdetail in sections A and B, respectively.

A. Joint MGRF Based Macula-Centered Foveal Image Segmentation

Let g={g(x):x∈R; g(x)∈Q} and m={l(x):x∈R; l(x)∈L} be a grayscale imagetaking values from Q, i.e., g:R→Q, with the associated region map takingvalues from L, i.e., m:R→L, respectively. R denotes a finite arithmeticlattice, Q is a finite set of integer gray values, and L is a set ofregion labels. An input OCT image, g, co-aligned to the trainingdatabase, and its map, m, are described with a joint probability model:P(g,m)=P(g|m)P(m)which combines a conditional distribution of the images given the mapP(g|m), and an unconditional probability distribution of mapsP(m)=P_(sp)(m)P_(v)(m). Here, P_(sp)(m) denotes a weighted shape prior,and P_(v)(m) is a Gibbs probability distribution with potentials V, thatspecifies a MGRF model of spatially homogeneous maps m.

(1) Shape Model P_(sp)(m): In order to account for the inhomogeneity ofthe OCT images, the shape information is incorporated in thesegmentation process. The shape model is constructed using OCT scansselected in such a way as to be representative and to capture thebiological variability of the whole data set. “Ground truth”segmentations of these scans were delineated under supervision of retinaspecialists. Using one of the optimal scans as a reference (no tilt,centrally located fovea), the others were co-registered using a thinplate spline (TPS). The shape prior was defined as:P _(sp)(m)=n p _(sp:y)(l)y∈RWhere, p_(sp:y)(l) is the pixel-wise probability for label l, and y isthe image pixel with gray level g. The same deformations were applied totheir respective ground truth segmentations, which were then averaged toproduce a probabilistic shape prior of the typical retina, i.e., eachlocation x in the reference space is assigned a prior probability P(m)to lie within each of the 12 layers' classes. The same deformations wereapplied to their respective ground truth segmentations, then averaged toproduce a probabilistic shape prior of the typical retina. The inputmedical image intended to be segmented (i.e., the initial slice) isfirst aligned to the shape database. The used alignment approachintegrates TPS with the multi-resolution edge tracking method thatidentifies control points for initializing the alignment process asshown in FIG. 2. More specifically, a coarse detail component of theundecimated discrete wavelet transform is used to locate changes in OCTreflectivity, where one zone transitions into another. The sharpest zoneboundaries in the normal or mildly pathological retina occur at thevitreous/NFL, ONL/ELM, and RPE/choroid boundaries. The center of thefovea is identified as the point where the vitreous/NFL boundary is atminimal distance from the ONL/ELM boundary. Control points areautomatically placed on each boundary at the foveal pit, and outwardtherefrom at regular intervals. A TPS transformation is used to warp theimage, bringing the control points into alignment with the correspondinglocations in the retinal atlas.

(2) Adaptive Model: In order to make the segmentation process adaptiveand not biased to only the shape information, a 1st-order intensitymodel P(g|m) was used for the empirical gray level distribution of theOCT images. The visual appearance of each label of the image is modeledby separating a mixed distribution of pixel intensities into individualcomponents associated with the dominant modes of the mixture. The modesare identified using the LCDG algorithm, which employs positive andnegative Gaussian components that is based on a modified version of theclassical Expectation Maximization (EM) algorithm. Then, a 2nd-orderMGRF model P_(v)(m) is used to improve the spatial homogeneity of thesegmentation. This model was identified using the nearest pixels'8-neighborhood and analytical bi-valued Gibbs potentials V that dependon the equality of the nearest pair of labels. This MGRF Potts modelthat accounts for spatial information was incorporated with the shapeand intensity information as explained in Section A.

B. Layers Segmentation 3-D Propagation

Referring now to FIG. 3, 3-D OCT retinal image data comprises aplurality of 2-D B-scans ranging from 1 to n, as shown in the dashedline enclosure on the left side of the figure. As the inter-subjectvariability for the retinal cross sections is very high compared tointra-subject variability, the obtained segmentation of the initialmidline B-slice, designated as “i” in this figure, is utilized as aninitial seed to guide the segmentation of neighboring slices in the±z-direction. To segment the remaining 3-D slices, a segmentationpropagation technique is developed based on a novel adaptive shape priorthat takes into account the mapped voxel locations in addition to thereflectivity values, such that B-slices i+1 and i−1 are segmented atleast in part based on the segmentation of B-slice i, slice i+2 issegmented at least in part based on the segmentation of slice i+1, slicei+3 is segmented at least in part based on the segmentation of slicei+2, and so forth until all slices 1 to n have been segmented. Unliketraditional shape models that depend only on the mapped voxel locationto calculate the probabilistic map, this 1st order adaptive intensitymodel is designed such that only the visually similar voxels willcontribute in the probability map calculations for the slice to besegmented to provide accurate segmentation results.

Referring now to FIG. 4, the segmentation process proceeds as follows:starting from the initial midline B-slice, the 12 retinal layers will besegmented as described above and assigned different labels, L (L=1 . . .12). Then moving backward and forward in the z-direction, each currentslice i is segmented referring to the previously segmented slice (i+1 ori−1), based on the direction. This procedure comprises first aligningthe reflectivity image of the current slice to its previously segmentedadjacent neighbor slice to obtain a deformation field that maps eachvoxel of the current slice to its neighbor. Second, the obtaineddeformation field is used to map each voxel to its correspondinglocation at the segmented neighboring slice. Third, at each voxel in theslice i, a N 1×N 2 window w (shown as solid line boxes overlaying theslices in FIG. 4) is generated around its mapped counterpart in adjacentslice (i+1 or i−1), then voxels in that window whose reflectivity valuesfall within a predefined tolerance ±τ are selected. If no voxels arefound, the window size is increased until such voxel(s) are found, ormaximum window size is reached (max window size shown as dotted lineboxes overlaying the slices). Fourth, the probability of each voxel tobe part of a specific layer is calculated based on the occurrence ofpositively labeled voxels from the total voxels in slice i+1 which arewithin the window whose reflectivity values are close to the voxel inslice i. Fifth, MGRF spatial refinement is then applied for the obtainedinitial map for each slice independently to improve the segmentationresult. Sixth, the whole volume is finally reconstructed, and 3-D-medianfilter is applied to the volume which improves the segmentationconsistency and surface smoothness. The above procedure is detailed inAlgorithm 1.

Algorithm 1: Steps of the Shape Prior Segmentation.

1) Segment the retinal layers in the midline slice following theprocedures in Section A.

2) For each slice i, i=1 to n

-   -   I. Use non-rigid registration to align the gray image for the        current slice (slice i) with the preceding/succeeding slice        (based on the direction) to obtain the deformation fields.    -   II. For each pixel v in slice i        -   (a) Transform v to the neighboring slice domain using the            obtained deformation field.        -   (b) Initialize a 2-D window, w, of size N_(1i)×N_(2i)            centered around the mapped voxel (v_(mapped)).        -   (c) Search w for pixels with corresponding reflectivity            value in the neighboring slice where reflectivity falls            within a predefined tolerance ±τ in w.        -   (d) If no pixels are found using Step (c), increase the size            of w and repeat step (c) until correspondences are found or            the maximum size allowed for w is reached.        -   (e) Calculate the shape probability for each retinal layer            at location r based on the found voxels and their labels.        -   End for    -   III. Assign pixel v the label with the highest probability.

End for

For clarification, please note that “slice i” refers to the initialslice in FIG. 3, and refers to the slice currently being segmented inFIG. 4 and Algorithm 1.

EXPERIMENTAL RESULTS: Ten 3-D OCT scans of 10 subjects were used to testthe accuracy of the disclosed method of retinal layer segmentation andvalidate the method against manual segmentation. The OCT scans of thesubjects were collected using Zeuss Cirrus HD-OCT 5000 with 5 OCT scansper each OCT volume. Subjects' range of age is from 32 to 50 years(mean±SD, 40±10.2 years). Retina expert specialists manually segmentedthe scans of retinal layers to construct a ground truth segmentation.

FIG. 5 displays b-slices from a sample 5-scan (i.e., consisting of 5b-slices) 3-D OCT volume segmented into 12 layers using the disclosedprocedure of segmentation. The segmentation process is accomplished inthree steps as shown in FIG. 5. The three steps are demonstrated below:

Step 1: Segmentation using joint-MGRF model to obtain the maculamid-slice (slice i, i.e., slice 3 out of 5).

Step 2: 3-D Segmentation propagation using slice i as a patient-specificatlas to segment both slices i−1 and i+1 (i.e., slices 2 and 4).

Step 3: 3-D Segmentation propagation by using slice i−1 (i.e., slice 2)as a patient-specific atlas to segment slice i−2 (i.e., slice 1) andusing slice i+1 (i.e., slice 4) as a patient-specific atlas to segmentslice i+2 (i.e., slice 5).

As should be readily understood, this process is expandable to anynumber of slices following the pattern of using a segmented slice as apatient-specific atlas for segmentation of the adjacent slice in thefollowing step. The instant segmentation approach has been found to bereliable for segmentation of challenging diseased cases such asage-related macular degeneration (AMD) and diabetic retinopathy wherethe layers' anatomy is distorted.

To evaluate the accuracy and robustness of the proposed approach, fourcommonly used evaluation metrics were used to compare the disclosedsegmentation approach with the ground truth and prior work. Thesemetrics are listed below:

(1) Dice Similarity Coefficient (DSC) which is a measure of the ratio ofshared segmentation between two images. It can be defined as follows:

${DSC} = \frac{2{{R\bigcap W}}}{{R} + {W}}$where R and W are the two segmentations to be compared. The higher theDSC value is, the more similar both segmentations are.

(2) 95-percentile bidirectional modified Hausdorff Distance (HD) thatmeasures the maximum distance between 2 matching points for 2 differentsegmentation techniques. The smaller the distance, the better thesegmentation. For two sets of boundary points (X, Y), HD is defined as:

HD(X, Y) = max [min (x − y)] x ∈ X  y ∈ Ywhere the 5% largest distances are removed, then the maximum of HD(X, Y)and HD(Y, X) is determined for each image.

(3) Unsigned Mean Surface Position Error (MSPE) that measures thedistance between boundaries in two different segmentations at each pointacross the images.

(4) Average Volume Difference (AVD) which measures the volume differencebetween obtained segmentation and the ground truth. A smaller volumedifference indicates a better segmentation.

In order to highlight the advantage of the approach disclosed herein, itis compared with a well-established 3-D automated segmentation approachas disclosed in Li, K., et al., “Optimal surface segmentation involumetric images—a graph-theoretic approach,” IEEE Transactions onPattern Analysis and Machine Intelligence, vol. 28, no. 1, pp. 119-134,2006. However, the approach in Li et al. can only segment 11 of the 12retinal layers. In order to have a fair comparison, 6th and 7th layersin the 12 retinal layers segmented using the instant approach are mergedto make the number of layers equivalent in both techniques.

FIG. 6 provides a visual comparison of segmentation of five retinalimages by ground truth, i.e., by retinal experts (top row), generatedusing the system disclosed herein (middle row), and generated by theapproach of Li et al. (bottom row). Table 1 provides a quantitativeevaluation of the results of the instant segmentation approach and Li etal. as compared to ground truth based on four evaluation metrics, DSC,HD, MSPE, and AVD, averaged over all subjects per each layer.

TABLE 1 Comparative segmentation accuracy between this proposed approachand Li et al., evaluated by DSC, HD, MSPE, and AVD. DSC (%) HD (voxels)MSPE (voxels) AVD (%) Metric Pro- Li et Pro- Li et Method Proposed Li etal. Proposed Li et al. posed al. posed al. Layer 82.51 ± 76.32 ± 3.81 ±8.02 ± 0.16 ± 1.44 ± 15.28 ± 41.17 ± 1 2.91 5.96 0.90 5.54 0.11 0.799.73 18.47 Layer 82.81 ± 77.23 ± 5.41 ± 5.65 ± 0.32 ± 0.72 ± 8.98 ± 6.95± 2 2.53 4.34 3.40 3.71 0.15 0.50 3.97 4.19 Layer 80.36 ± 77.94 ± 8.19 ±5.02 ± 0.30 ± 0.75 ± 16.22 ± 6.15 ± 3 3.30 4.19 4.28 3.49 0.14 0.4415.48 3.29 Layer 80.35 ± 77.80 ± 7.46 ± 4.02 ± 0.46 ± 0.50 ± 8.91 ±14.39 ± 4 3.30 3.56 3.72 1.77 0.24 0.28 5.94 3.16 Layer 78.93 ± 76.77 ±15.33 ± 5.92 ± 1.86 ± 1.24 ± 22.41 ± 35.64 ± 5 3.51 3.58 6.07 3.27 0.820.44 16.02 14.87 Layer 84.64 ± 81.77 ± 3.33 ± 2.06 ± 0.83 ± 0.23 ± 11.04± 5.28 ± 6 3.72 2.15 2.52 0.55 0.37 0.09 5.55 4.18 Layer 84.51 ± 80.89 ±3.60 ± 6.62 ± 0.55 ± 1.85 ± 20.91 ± 31.60 ± 7 3.74 2.13 4.68 1.18 0.680.34 14.48 3.34 Layer 84.42 ± 80.04 ± 3.25 ± 7.34 ± 0.26 ± 2.57 ± 20.25± 24.48 ± 8 3.71 2.22 2.65 0.96 0.17 0.58 10.51 14.24 Layer 84.10 ±79.49 ± 4.20 ± 7.95 ± 1.02 ± 2.48 ± 56.73 ± 36.89 ± 9 4.00 2.26 2.421.77 0.78 0.93 63.79 5.03 Layer 83.70 ± 79.08 ± 4.79 ± 14.28 ± 0.94 ±1.17 ± 26.61 ± 73.24 ± 10 4.11 15.51 1.59 5.59 0.63 0.40 14.44 10.74Layer 83.31 ± 78.08 ± 3.15 ± 13.13 ± 1.29 ± 2.27 ± 15.92 ± 41.80 ± 114.06 15.07 0.29 4.93 1.99 1.27 12.48 15.75 Aver- 82.69 ± 78.67 ± 5.68 ±7.27 ± 0.73 ± 1.38 ± 20.30 ± 28.87 ± age 3.37 4.72 1.90 2.01 0.21 0.3511.94 2.93

The above results indicate overall superior performance of the instantmethod in terms of DSC, HD, MSPE, and AVD as compared to the earlierapproach disclosed in Li et al. Using paired t-test for statisticalanalysis shows a significant advantage of this approach over the earlierapproach in terms of all metrics as confirmed by p-values <0.05.

Various aspects of different embodiments of the present disclosure areexpressed in paragraphs X1 and X2 as follows:

X1: One embodiment of the present disclosure includes a method forsegmenting a medical image comprising: receiving a volumetric medicalimage comprising a plurality of slices, each slice being adjacent to atleast one other slice in the image; selecting an initial slice;segmenting the initial slice based at least in part on a constructedshape model; and segmenting at least one slice adjacent to the initialslice based at least in part on the segmented initial slice.

X2: Another embodiment of the present disclosure includes a method forsegmenting a 3-D medical image comprising: receiving a 3-D medicalimage, the 3-D medical image comprising an array of adjacent 2-D medicalimages; segmenting an initial 2-D medical image based at least in parton a constructed shape model; and segmenting a 2-D medical imageadjacent to the previously segmented 2-D medical image based at least inpart on the previously segmented 2-D medical image.

Yet other embodiments include the features described in any of theprevious paragraphs X1 or X2 as combined with one or more of thefollowing aspects:

Wherein the medical images are retinal images.

Wherein the medical images are optical coherence tomography images.

Wherein the initial slice has two adjacent slices.

Wherein the volumetric medical image is a 3-D medical image and whereinthe plurality of slices are a plurality of 2-D medical images.

Wherein segmenting the initial slice includes aligning the initial sliceto the constructed shape model.

Wherein segmenting the initial slice further includes applying a jointmodel to the initial slice subsequent to alignment.

Wherein segmenting the at least one slice adjacent to the initial sliceincludes aligning the at least one slice to the initial slice.

Further comprising, after segmenting the initial slice, applying a labelto each segmented layer in the initial slice.

Wherein each segmented layer corresponds to a retinal layer.

Wherein segmenting the at least one slice adjacent to the initial slicebased at least in part on the segmented initial slice includes, for apixel in the at least one slice, transforming the pixel to the initialslice, initializing a window, searching within the window for pixelswith a corresponding value in the initial slice, and calculating a shapeprior probability based on the labels of found pixels with correspondingvalues, and labeling the pixel in the at least one slice based on theshape prior probability.

Wherein the pixel in the at least one slice has a reflectivity value andwherein searching within the window for pixels with the correspondingvalue in the initial slice comprises searching within the window forpixels with corresponding reflectivity values in the initial slice.

Further comprising, after the step of segmenting the 2-D medical image,repeating the prior step until all 2-D medical images in the array aresegmented.

Wherein the 3-D medical image is a retinal image depicting at least afovea, and wherein the initial 2-D medical image extends through thefovea.

Wherein segmenting the initial 2-D medical image includes aligning theinitial 2-D medical image to the constructed shape model and applying ajoint model to the initial 2-D medical image subsequent to alignment.

Wherein the initial 2-D medical image depicts an anatomical feature andwherein the constructed shape model is constructed from a database ofimages of the anatomical feature.

Wherein the anatomical feature is a fovea.

Wherein segmenting the 2-D medical image based at least in part on thepreviously segmented 2-D medical image includes, for a pixel in the 2-Dmedical image, determining a value for the pixel, transforming the pixelto the previously segmented 2-D medical image, initializing a window,searching within the window for pixels with a corresponding value in thepreviously segmented 2-D medical image, calculating a shape priorprobability based on labels of found pixels with corresponding values,and labeling the pixel in the 2-D medical image based on the shape priorprobability.

Wherein the value for the pixel is a reflectivity value for the pixel.

The foregoing detailed description is given primarily for clearness ofunderstanding and no unnecessary limitations are to be understoodtherefrom for modifications can be made by those skilled in the art uponreading this disclosure and may be made without departing from thespirit of the invention. While the disclosed invention has beendescribed primarily in connection with the segmentation of 3-D OCTretinal images, it should be understood that the disclosed segmentationtechniques may be usable with segmentation of 3-D medical imagesobtained using different imaging modalities or depicting differentanatomical features.

What is claimed is:
 1. A method for segmenting a medical imagecomprising: receiving a volumetric medical image comprising a pluralityof slices, each slice being adjacent to at least one other slice in theimage; selecting an initial slice; segmenting the initial slice based atleast in part on a constructed shape model; applying a label to eachsegmented layer in the initial slice; and segmenting at least one sliceadjacent to the initial slice based at least in part on the segmentedinitial slice wherein segmenting the at least one slice adjacent to theinitial slice based at least in part on the segmented initial sliceincludes, for a pixel in the at least one slice, transforming the pixelto the initial slice, initializing a window, searching within the windowfor pixels with a corresponding value in the initial slice, andcalculating a shape prior probability based on the labels of foundpixels with corresponding values, and labeling the pixel in the at leastone slice based on the shape prior probability.
 2. The method of claim1, wherein the medical images are retinal images.
 3. The method of claim1, wherein the medical images are optical coherence tomography images.4. The method of claim 1, wherein the initial slice has two adjacentslices.
 5. The method of claim 1, wherein the volumetric medical imageis a 3-D medical image and wherein the plurality of slices are aplurality of 2-D medical images.
 6. The method of claim 1, whereinsegmenting the initial slice includes aligning the initial slice to theconstructed shape model.
 7. The method of claim 6, wherein segmentingthe initial slice further includes applying a joint model to the initialslice subsequent to alignment.
 8. The method of claim 1, whereinsegmenting the at least one slice adjacent to the initial slice includesaligning the at least one slice to the initial slice.
 9. The method ofclaim 1, wherein each segmented layer corresponds to a retinal layer.10. The method of claim 1, wherein the pixel in the at least one slicehas a reflectivity value and wherein searching within the window forpixels with the corresponding value in the initial slice comprisessearching within the window for pixels with corresponding reflectivityvalues in the initial slice.
 11. A method for segmenting a 3-D medicalimage comprising: receiving a 3-D medical image, the 3-D medical imagecomprising an array of adjacent 2-D medical images; segmenting aninitial 2-D medical image based at least in part on a constructed shapemodel; and segmenting a 2-D medical image adjacent to the segmentedinitial 2-D medical image based at least in part on the segmentedinitial 2-D medical image wherein segmenting the 2-D medical image basedat least in part on the previously segmented 2-D medical image includes,for a pixel in the 2-D medical image, determining a value for the pixel,transforming the pixel to the previously segmented 2-D medical image,initializing a window, searching within the window for pixels with acorresponding value in the previously segmented 2-D medical image,calculating a shape prior probability based on labels of found pixelswith corresponding values, and labeling the pixel in the 2-D medicalimage based on the shape prior probability.
 12. The method of claim 11,further comprising, after the step of segmenting the 2-D medical image,repeating the step of segmenting the 2-D medical image until all 2-Dmedical images in the array are segmented.
 13. The method of claim 11,wherein the 3-D medical image is a retinal image depicting at least afovea, and wherein the initial 2-D medical image extends through thefovea.
 14. The method of claim 11, wherein segmenting the initial 2-Dmedical image includes aligning the initial 2-D medical image to theconstructed shape model and applying a joint model to the initial 2-Dmedical image subsequent to alignment.
 15. The method of claim 11,wherein the initial 2-D medical image depicts an anatomical feature andwherein the constructed shape model is constructed from a database ofimages of the anatomical feature.
 16. The method of claim 15, whereinthe anatomical feature is a fovea.
 17. The method of claim 11, whereinthe value for the pixel is a reflectivity value for the pixel.